References

What is running in production.

AI projects live with clients — described concretely, with results. Not a brochure.

Success Story· Energy

Smart Voicebot for Future-Ready Customer Service

enersuisse

Challenge
Today’s customers expect fast and effortless solutions. Previously, simple requests such as ordering an invoice copy or setting up a payment plan had to be forwarded by phone to customer service agents, occasionally resulting in waiting times and additional administrative effort.
Solution
With the introduction of the digital assistant, customers now benefit from a modern self-service solution. Provided all required criteria are met, the system can automatically set up a payment plan or send an invoice copy. At the same time, customers always have the option of being connected directly to a customer service representative.
Outcome
Standard requests are processed automatically, reducing service times, relieving employees of repetitive tasks, and increasing customer satisfaction.
DatabricksSAP BTPMLflowVoicebotOmnichannelMulti-AgentRAGMCPSwiss DPA
Success Story· Energy

Faster to the right answer in customer service — AI chatbot PoC

Energie Wasser Bern (ewb)

Challenge
A large, heterogeneous knowledge base in customer service — research takes time and gives different results depending on experience.
Solution
A proof of concept for an internal chatbot that understands enquiries, identifies the relevant content, and generates high-quality draft answers.
Outcome
A solid evaluation of feasibility and value as the basis for the next step into production.
RAGKnowledge Base
Success Story· Others

Answers on SAP data in seconds — Chatbot Summy

inpeek

Challenge
Ask complex questions of structured data in SAP HANA, map terms reliably to existing entities, and evaluate quality automatically.
Solution
An AI-powered chatbot built on SAP BTP, fully integrated into Summarix, with automated, objective evaluation after every iteration.
Outcome
Sales and delivery teams answer questions about skills, projects and references in seconds — right in the tool.
SAP BTPSAP HANAStructured DataEvaluation
Success Story· Others

Joule A2A: Integration Chatbot Summy als Pro-Code Agent in Joule

inpeek

Challenge
With the emergence of SAP Joule, the desire arose to interact with Summy directly through SAP Joule instead of using a separate chat interface. To enable the standardized use of Summy within SAP Joule, three prerequisites had to be met: integration with SAP Joule as the central user interface, standardized communication between AI agents, and the use of SAP BTP's model and security services.
Solution
SAP Joule recently introduced support for custom pro-code agents developed in Python. We chose this approach for Summy: the agent remains an independent service with its own business logic, while SAP Joule serves as the central user interface. Integration is achieved through A2A, allowing Summy to expose its capabilities directly within SAP Joule.
Outcome
Summy is now fully integrated into SAP Joule. Users can ask questions about projects, employees, and competencies directly within SAP Joule without requiring a separate user interface or tool. The A2A standard also provides the foundation for a scalable multi-agent architecture. Additional agents, such as those for tender analysis, consultant matching, or CV generation, can be connected without modifying the existing architecture.
SAP AI CoreSAP JouleSAP BTPVector EngineSAP Knowledge GraphA2A
Success Story· Energy

Intelligent Email Triage for More Efficient Customer Service

enersuisse

Challenge
Every day, enersuisse receives more than 500 customer emails covering a wide variety of requests. The previous solution for categorisation and assignment was reaching its limits, meaning that the actual handling of inquiries remained heavily dependent on manual work.
Solution
The newly implemented email triage fundamentally changes this. It provides support long before a message reaches the correct mailbox. The system automatically performs intent recognition and customer identification, clearly summarises the email content, and directly provides employees with a well-founded response suggestion.
Outcome
For employees, this means fewer manual tasks, less effort spent on sorting and forwarding emails, and a stronger basis for professional review. The technology operates in the background, while responsibility continues to remain with the employees.
DatabricksSAP AI CoreLLMMLflowIntent dedection
Read on inpeek.ch
Success Story· Energy

From Unstructured Support Emails to a Productive RAG Knowledge Base

gPlug

Challenge
With the increasing adoption of gPlug products, the volume of support requests increased significantly. Many requests were repetitive or referred to information already available in previous emails, documentation, or on the website. At the same time, this knowledge existed in an unstructured form within email inboxes and could only be utilised with considerable manual effort. This led to inefficient processes and extended response times in support operations.
Solution
We developed a fully Swiss-operated AI solution based on Retrieval Augmented Generation (RAG), which automatically unlocks and makes historical support knowledge usable. Support emails are automatically extracted, processed by a Large Language Model, cleaned of personal data and irrelevant content, and transformed into structured question-and-answer pairs. From this information, compact knowledge snippets are generated and stored in a vector database together with content from technical documentation and the company website. Through Open WebUI, employees gain access to various preconfigured AI agents, including a Q&A assistant for rapidly answering support questions and an email agent for creating response drafts. All model inference is performed using open-source LLMs hosted by a Swiss provider, ensuring data protection, data sovereignty, and information security at all times.
Outcome
With the introduction of the solution, gPlug can, for the first time, make the knowledge previously distributed across support emails centrally available, structured, and usable across the entire organisation. As a result, support processes become significantly more efficient: responses can be created faster and more consistently, existing knowledge can be reused across channels, and the quality of information provided is sustainably improved through context-based AI support. Standard inquiries can be processed semi-automatically, allowing support employees to focus more on complex issues and value-adding tasks. At the same time, the fully Swiss-operated solution ensures maximum data sovereignty, compliance with all data protection requirements, and complete independence from foreign legislation such as the US Cloud Act.
LLMVector databaseRAGOpen WebUIKnowledge ManagementSemantic SearchSwiss Hosting
Read on inpeek.ch
Success Story· Energy

AI-Powered Email Automation for a Swiss Energy Utility

enersuisse

Challenge
Although incoming customer inquiries could already be automatically classified and routed to the appropriate departments, the actual processing remained largely manual. Valuable knowledge from thousands of historical support requests was scattered across different systems and mailboxes, making it difficult for employees to access and leverage.
Solution
As part of DigiKI Omni, an AI-based knowledge platform was developed to automatically unlock historical support knowledge and make it usable for daily customer communication. Historical support emails are analyzed, cleaned, structured, and stored together with technical documentation in a central knowledge base. Building on this foundation, an AI-powered email agent supports employees in handling customer inquiries by generating context-aware response drafts and automatically providing relevant knowledge from previous cases. The solution leverages a central AI Core and modern RAG technology.
Outcome
Knowledge previously hidden in support emails becomes available across the organization for the first time. Employees receive high-quality response suggestions more quickly, can build on existing solutions, and handle customer inquiries more efficiently and consistently. At the same time, a scalable knowledge base is created, forming the foundation for further process automation and sustainably reducing the workload of customer service teams.
RAGOpen WebUIVector databaseGen AI
Success Story· Others

From SiNa Scan to SAP – Traceable Information Extraction

PoC

Challenge
Safety certificates (SiNa) are submitted to the utility company either as scans or handwritten forms. Anyone who needs the values in the ERP system (e.g., SAP IS-U) has to enter them manually. This takes time and is a source of errors.
Solution
A vision-language model reads the information and marks the location where each value was found. In the user interface, a person can review the extracted values against the original document and correct anything that is inaccurate. Approved values are transferred directly to SAP IS-U. Processing is carried out on Swiss infrastructure and can also be deployed on-premises on the customer's hardware if required.
Outcome
The solution provides validated values for SAP IS-U. During review in the tool, users can see the source location of each value in the document, while only the confirmed value is transferred to SAP. This significantly improves master data quality and enables further automation based on this data. Data protection requirements are fully met, and employees can focus on more engaging tasks.
Vision Language ModelSAP AI CoreInformations ExtraktionSAP IS-UOn-PremStructured Data